Milvus 3.0 Walkthrough: Swapping Embedding Models with Zero Downtime
The non-obvious insight is that embedding model swaps are a data-migration problem: old vectors must stay searchable until the new index is fully backfilled and validated, or search quality collapses mid-rollout. Teams running RAG or semantic search in production can follow the walkthrough to plan migrations with built-in evaluation and rollback points, comparing their own Recall numbers against the SciFact baseline before committing.
